AoI-Based Scalable Edge Computing Resource Allocation in Heterogeneous IIoT Systems

IEEE Wireless Communications and Networking Conference, pp. 1–6

Abstract

Edge computing is a sustainable network paradigm that supports resource constrained Industrial Internet of Things (IIoT) devices in performing data-driven tasks. The distributed architecture of computing resources, combined with the heterogeneous and sporadic characteristics of IIoT tasks, presents a substantial challenge to achieving energy-efficient and low-latency task execution. In this paper, we demonstrate that modeling the Age of Information (AoI) as an exponential function can significantly enhance the performance of task scheduling and execution in edge computing systems. Furthermore, we introduce a low over-head Multi-Agent Deep Reinforcement Learning (DRL) algorithm with No Observation (MA-NO) for distributed edge computing resource allocation, where agents (IIoT devices) operate independently without communicating with each other. However, to prevent the divergence of the agents and ensure a Pareto-optimal decision-making, we adopt a common reward that is shared among all agents. That is, all agents receive the same reward, based on their collective performance. Simulation results show that our proposed AoI model reduces the average execution latency of the tasks by 35.2% compared to a linear AoI ones. Furthermore, our proposed MA-NO algorithm reduces the total energy consumption of the IIoT devices by 59.9% and 63.4% compared to the single-agent DRL algorithm and multi-agent DRL algorithm with partial observations, respectively.

Authors 4

1. Daniel S. Zakamulin Aachen Chair for Distributed Signal Processing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany

2. Mohsen Pourghasemian Aachen Chair for Distributed Signal Processing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany

3. Firooz B. Saghezchi Aachen Chair for Distributed Signal Processing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany

4. Haris Gacanin Aachen Chair for Distributed Signal Processing

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair for Distributed Signal Processing,Aachen,Germany